1 citations · 2 across the 5 of their papers we have counts for
5 papers
Generative Modeling via Kernelized Stochastic Interpolants
Florentin Coeurdoux, Etienne Lempereur, Nathanaël Cuvelle-Magar +2
We develop a kernel method for generative modeling within the stochastic interpolant framework, replacing neural network training with linear systems. The drift of the generative S…
MGD: Moment Guided Diffusion for Maximum Entropy Generation
Etienne Lempereur, Nathanaël Cuvelle--Magar, Florentin Coeurdoux +2
Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification from…
Effective Energy, Interactions And Out Of Equilibrium Nature Of Scalar Active Matter
Antonin Brossollet, Etienne Lempereur, Stéphane Mallat +1
Estimating the effective energy, of a stationary probability distribution is a challenge for non-equilibrium steady states. Its solution could offer a novel framewor…
Hierarchic Flows to Estimate and Sample High-dimensional Probabilities
Etienne Lempereur, Stéphane Mallat
Finding low-dimensional interpretable models of complex physical fields such as turbulence remains an open question, 80 years after the pioneer work of Kolmogorov. Estimating high-…
Conditionally Strongly Log-Concave Generative Models
Florentin Guth, Etienne Lempereur, Joan Bruna +1
There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees. The former suffer from mode collap…